Are Protected Values Quantity Sensitive?

1. Are Protected Values Quantity Sensitive? Rumen Iliev Northwestern University

2. Overview • Sacred/protected values • Definitions • Properties • Challenges • Context effects in a choice set • Examples • Connection to protected values • Empirical study on context effects in the moral domain • Conclusion • Future directions

3. Sacred/Protected Values • Tetlock et al. (2000) defined sacred values as “any value that a moral community implicitly or explicitly treats as possessing infinite and transcendental significance that precludes comparisons, tradeoffs, or indeed any other mingling with bounded or secular values”. • Baron and Spranca (1997) defined protected values as “… those that resist tradeoffs with other values, particularly with economic values”. • The rejection of tradeoffs is a challenge for utility based models in decision making

4. Sacred/Protected Values: Properties • Sacred values appear to have distinctive properties that make them different from secular values: • infinite utility • omission bias • quantity insensitivity • denial of the need for tradeoffs • moral obligation • emotional responses

5. Sacred/Protected Values: Challenges • Challenges for research on sacred values • By definition a person who endorses sacred/protected values would reject a tradeoff (at least of sacred for secular goods) • Most of the tasks exploring the properties of protected values rely on explicit tradeoffs • Would you harm 5 species in order to save 10 other species? • Is destroying 1 acre of old grown forest less wrong than destroying 2 acres? • How much money would you accept in order to give up your land?

6. Sacred/Protected Values: Alternative approaches • Alternative approaches • Indirect influence on cognitive processes • Stroop interference • Heuristics and Biases • Representativeness and Anchoring • Context Effects • Influencing choice preferences via “decoy” alternatives

7. Attraction Effect • Two alternative choice set • P(alt.2)<P(alt.3)

8. Attraction Effect • Two alternative choice set • P(alt.2)<P(alt.3) • Three alternative choice set (alt.1 is a decoy) • But P(alt.2|alt.1) >P(alt.3|alt.1)

9. Compromise effect • Alt. 2 becomes a compromise when alt.1 is added to the choice set • P(alt.2)<P(alt.3) P(alt.2|alt.1) >P(alt.3|alt.1)

10. Why Context Effects Are Interesting? • For traditional judgment and decision making: • Independence of irrelevant alternatives: • The decision maker has complete preference order of all options • Pragmatic purposes • One can influence market purchases • For morally motivated decision making: • If sacred/protected values imply infinite utility on a particular dimension we should not expect context effects when moral issues are at stake • Even thought there is significant amount of research on moral-secular tradeoffs, relatively little has been done on moral-moral tradeoffs

11. Experiment 1 • Participants: • 77 NU undergraduates participated for a course credit • Stimuli: • 12 choice sets: • Abortions • Endangered species • Wrongly convicted prisoners • Starving children • Example: Tradeoff between preventing more abortions or saving more starving children • 6 attraction scenarios and 6 compromise scenarios • Protected values questions • Action X is unacceptable no matter what the benefits.

12. Example: Compromise Scenario Condition 1 • Plan A will prevent 2000 abortions and save 10 species • Plan B will prevent 3000 abortions and save 8 species • Plan C will prevent 4000 abortions and save 6 species Condition 2 • Plan A will prevent 1000 abortions and save 12 species • Plan B will prevent 2000 abortions and save 10 species • Plan C will prevent 3000 abortions and save 8 species

13. Example: Compromise

14. Example: Attraction Scenario Condition 1 • Plan A will prevent 2000 abortions and save 10 species • Plan B will prevent 3000 abortions and save 8 species • Plan C will prevent 2900 abortions and save 7 species Condition 2 • Plan A will prevent 1900 abortions and save 9 species • Plan B will prevent 2000 abortions and save 10 species • Plan C will prevent 3000 abortions and save 8 species

15. Example: Attraction

16. Overview of results • Very strong attraction effects but essentially no compromise effects • Little difference as a function of whether one or more dimensions involve a sacred value

21. Experiment 1: Summary • PVs were significant predictor of subjects’ choice (R2 = .22, p<.05) • Overall attraction effect, but no reliable compromise effect • Attraction effect was found even in case when people had PVs on only one of the dimensions

22. Experiment 1: Limitations • Randomized between-subject condition but not within-subjects • Positive framed scenarios but negatively framed protected values • abortions prevented/ species saved • actions prohibited • No control comparison • may be compromise effects are less reliable and more difficult to detect

23. Experiment 2 • Within-subject randomization • Negatively framed scenarios • Damage control scenarios where your plan will cause more damage or another dimension • Updated dimensions • abortions, dolphins, homeless people, starving children (total of 18 scenarios) • Secular choices • Laptops, cameras, iPods, tires (total of 8 scenarios) • Participants: • 61 NU undergraduates participated for a course credit

28. Χ2 statistics

29. Summary • PVs predict the chosen alternative • Scenarios that involve morally relevant items show typical attraction effect but no consistent compromise effects • Protected values did not suppress attraction effects • Morally relevant scenarios did not show compromise effect when none of the dimensions involved protected values

30. Conclusion • Reliable attraction effect is consistent with the hypothesis for implicit trade offs even when sacred values are included • From modeling perspective, the attraction effect suggests that sacred values could be modeled using dimensional weights • The difference between attraction and compromise effects could be due to differential impact on cognitive processes when morally relevant tradeoffs are considered • value-shift explanations dealt with attraction effect only • reasons based choice and loss aversion explanations were focused on compromise effect only • From pragmatic perspective, adding a decoy alternative might contribute for approval or disapproval of a proposed plan

31. Future Directions • Adding a justification condition • Context effects in voting behavior • adding a third candidate during an election race • Context effect in believe propagation • Interactions between neighboring nodes in a social/expert network

32. Acknowledgments • Doug Medin • Andrzej Tarlowski • Craig Joseph • Dan Bartels • Sara Unsworth • Sonya Sachdeva • Will Bennis